Role Description As a Data Architect, you will be responsible for designing, deploying, and governing modern data architectures that support enterprise-scale analytics, reporting, and operational use cases. You’ll define how data is stored, integrated, accessed, and secured across systems — aligning data strategy with business priorities. You will collaborate with both business and technology stakeholders to design scalable and efficient data platforms, define data modeling standards, and enable seamless integration across the enterprise. Your work will ensure data integrity, security, and usability, serving as a foundation for advanced analytics and AI initiatives. What You'll Do - Develop and maintain enterprise data architecture, including models, flow diagrams, and integration frameworks - Define and enforce architecture standards, governance models, and documentation practices - Design scalable, flexible solutions that align with modern data warehousing and cloud-native best practices - Drive the design and implementation of data integration pipelines and APIs across systems - Lead efforts in data modeling, database design, and architecture optimization - Collaborate with data engineers, analysts, and developers to ensure architectural consistency and performance - Mentor and provide technical direction to engineers on the Data Practice team, including full-stack engineers building applications on top of the data foundation - Oversee data quality, lineage, and security strategies across the organization - Provide guidance on big data technologies, ETL platforms, and modern cloud ecosystems - Support strategic decisions through architectural reviews, proofs of concept, and solution roadmaps Qualifications - 7+ years of experience in data architecture, data engineering, or related roles in complex environments - Demonstrated experience designing robust technical solutions for clients or customers - Experience serving as a technical lead - mentoring, coaching, and setting technical direction for data engineers, analysts, and other technical resources - Strong background in data modeling (conceptual, logical, physical), warehousing (e.g., Snowflake, Redshift, BigQuery), and database design - Familiarity with modern data transformation practices (e.g., dbt) and dimensional modeling patterns, including Slowly Changing Dimensions (SCD) - Proficient in data modeling tools (e.g., ER/Studio, Erwin) and relational/NoSQL databases (e.g., SQL Server, Oracle, MongoDB) - Fluent with AI coding agents (e.g., Claude Code, Cursor) as production accelerators, with the credibility to evaluate and coach engineers on how they use them - Skilled in applying AI/ML to complex data challenges, including designing architectures for generative AI and RAG solutions, and automating data pipelines - Experience with CRM and/or ERP ecosystems (e.g., Salesforce, NetSuite) and analyzing data to uncover quality issues, identify revenue leakage and/or drive operational efficiency - Knowledge of modern cloud data stacks (e.g., Azure, AWS, GCP) - Hands-on experience with ETL/integration tools (e.g., Talend, Informatica) - Familiarity with building and/or designing reporting solutions for business stakeholders (e.g., Power BI, Tableau) - Comfortable contributing to business development activities such as supporting proposals, scoping projects, and growing accounts - Strategic mindset with a focus on business value, scalability, and performance - Excellent communication skills and ability to influence cross-functional stakeholders - Adaptable, collaborative, and continuously improving in a fast-paced delivery environment Benefits - Flexible, remote-first work environment with high-performance expectations and autonomy. - Semi-annual team offsites — we come together in person at least twice a year to connect, recharge, and do the work that's better face-to-face. - A team that takes AI tooling seriously and expects you to integrate it into your work. - High-autonomy, high-ownership work across the full arc of real client problems. - Access to the full modern data and AI stack — no one-tool shops. - Exposure to top-tier private equity firms and their portfolio companies.